Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The aerosol fine-mode fraction (FMF) is valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable over land. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1<span class="inline-formula"><sup>∘</sup></span> spatial resolution covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361 089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error (RMSE) of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the <span class="inline-formula">±</span>20 % expected error (EE) envelopes was 79.15 %. Moreover, the out-of-site validation from the Surface Radiation Budget (SURFRAD) observations revealed that the RMSE of Phy-DL FMF is 0.144 (72.50 % of the results fell within the <span class="inline-formula">±</span>20 % EE). Phy-DL FMF showed superior performance over alternative deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95 % significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine-mode and coarse-mode aerosol changes. The datasets can be downloaded from <a href="https://doi.org/10.5281/zenodo.5105617">https://doi.org/10.5281/zenodo.5105617</a> (Yan, 2021).

Highlights

  • Evaluating the impact of anthropogenic aerosols on climate change and human health relies on the ability to separate the proportion of anthropogenic aerosols from the total aerosol loading (Anderson et al, 2005; Zheng et al, 2015)

  • Satellite remote sensing can provide global-scale data on aerosol content that are represented by the aerosol optical depth 40 (AOD), accurate monitoring of anthropogenic aerosols is still a major challenge. This is because a key parameter called the aerosol fine-mode fraction (FMF), which is used for discriminating anthropogenic aerosols from natural ones (Bellouin et al, 2005), has been regarded as highly unreliable according to satellite-based AOD retrievals, especially over land (Levy et al, 2013; Yan et al, 2017; Liang et al, 2021; Yang et al, 2020; Zang et al, 2021a)

  • deep learning (DL) FMFs with Aerosol Robotic Network (AERONET) FMFs, we first evaluated the overall performance of physical and deep learning models (Phy-DL)

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Summary

Introduction

Evaluating the impact of anthropogenic aerosols on climate change and human health relies on the ability to separate the proportion of anthropogenic aerosols from the total aerosol loading (Anderson et al, 2005; Zheng et al, 2015). Satellite remote sensing can provide global-scale data on aerosol content that are represented by the aerosol optical depth 40 (AOD), accurate monitoring of anthropogenic aerosols is still a major challenge. This is because a key parameter called the aerosol fine-mode fraction (FMF), which is used for discriminating anthropogenic aerosols from natural ones (Bellouin et al, 2005), has been regarded as highly unreliable according to satellite-based AOD retrievals, especially over land (Levy et al, 2013; Yan et al, 2017; Liang et al, 2021; Yang et al, 2020; Zang et al, 2021a). No multi-angle and multi-spectral polarized information, Lipponen et al (2018) noted that MODIS-based FMF retrievals using physical methods still suffer from these major limitations

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